This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

3D‐printed scaffolds loaded with healing directed agents could be employed for better treatment outcome in regenerative dentistry. The aim of this study was to fabricate and characterize simple 3D‐printed poly lactic acid (PLA) scaffolds coated with nanoHydroxyapatite (nHA), Naringin (NAR), or their combination, and testing their morphological, chemical, mechanical, antibacterial, biocompatible and bioactive properties. Methodology: Two variants pore sizes, 300 and 700 μm, of 3D‐printed PLA disc scaffolds measuring (10 × 1 mm) were fabricated. These scaffolds were dip‐coated with nHA, NAR, or both (nHA/NAR). Field emission scanning electron microscopy (FeSEM), energy‐dispersive X‐ray spectroscopy (EDX), Fourier transfo

Background: The demand for esthetic orthodontic appliances is increasing; so the esthetic orthodontic archwires were introduced. Among them, Teflon and Epoxy coated stainless steel archwires. The amount of force available from the archwire depends on the structural properties and susceptibility to corrosion. All metallic alloys are changed during immersion in artificial saliva, chlorhexidine mouthwash andtoothpaste, but their behaviors differ from one type to another. They corrode at different rates, which lead to decrease the amount of force applied to the teeth. This in vitro study was designed to evaluate the corrosion pits in stainless steel archwires coated with Teflon and with Epoxy in dry and after immersion in artificial saliva, chl

Background: Debonding orthodontic brackets and removal of residual bonding material from the enamel surface include critical steps that may cause enamel damage. The aim of the present study was to evaluate and compare the site of bond failure and enamel surface damage after debonding of three types of esthetic brackets (composite, ceramic, sapphire) bonded with light cure composite and resin-modified glass ionomer adhesive. Materials and methods: Seventy two maxillary premolars teeth were divided into three groups each group consisted of 24 teeth according to the type of brackets. Each group was subdivided into two subgroups (12 teeth for each) according to the bonding material that was used. After 7 days of bonding procedure, the brackets